We proposed a two-branch multitask learning convolutional neural network to solve two different but related tasks at the same time. Our main task is to predict occult invasive disease in biopsy proven Ductal Carcinoma in-situ (DCIS), with an auxiliary task of segmenting microcalcifications (MCs). In this study, we collected digital mammography from 604 patients, 400 of which were DCIS. The model used patches with size of 512×512 extracted within a radiologist masked ROIs as input, with outputs including noisy MC segmentations obtained from our previous algorithms, and classification labels from final diagnosis at patients’ definite surgery. We utilized a deep multitask model by combining both Unet segmentation networks and prediction classification networks, by sharing first several convolutional layers. The model achieved a patch-based ROC-AUC of 0.69, with a case-based ROC-AUC of 0.61. Segmentation results achieved a dice coefficient of 0.49.
Detecting microcalcification clusters in mammograms is important to the diagnosis of breast diseases. Previous studies which mainly focused on supervised methods require abundant annotated training data but these data are usually hard to acquire. In this work, we leverage unsupervised convolutional autoencoders and structural similarity (SSIM) based post-processing to detect and localize microcalcification clusters in full-field digital mammograms (FFDMs). Our models were trained by patches extracted from 3,632 normal cases, in total with 16,702 mammograms. Evaluations were conducted in three aspects, including patch-based anomaly detection, pixel-wise microcalcification localization, and microcalcification cluster detection. Specifically, the receiver operating characteristic (ROC) analysis was used for patch-based anomaly detection. Then, a pixel-wise ROC analysis and a cluster-based free-response ROC (FROC) analysis were performed to assess our detection algorithms of individual microcalcifications and microcalcification clusters, respectively. We achieved a pixel-wise AUC of 0.97 as well as a cluster-based sensitivity of 0.62 at 1 false positive per image and 0.75 at 2.5 false positives per image. Both qualitative and quantitative results demonstrated the effectiveness of our method.
Detection of suspicious breast cancer lesion in screening mammography images is an important step for the downstream diagnosis the of breast cancer. A trained radiologist can usually take advantage of multi-view correlation of suspicious lesions to locate abnormalities. In this work, we investigate the feasibility of using a random image pair of the same breast from the same exam for the detection of suspicious lesions. We present a novel approach to utilize a single shot detection system inspired by You only look once (YOLO) v1 to simultaneously process a primary detection view and a secondary view for the localization of lesion in the primary detection view. We used a combination of screening exams from Duke University Hospital and OPTIMAM to conduct our experiments. The Duke dataset includes 850 positive cases and around 10,000 negative cases. The OPTIMAM dataset includes around 350 cases. We observed a consistent left shift of the Free-Response Receiver Operating Characteristic (FROC) curve in the multi-view detection model compared to the single-view detection model. This result is promising for future development of automated lesion detection systems focusing on modern full-field digital mammography (FFDM).
Detection and localization of microcalcification (MC) clusters are very important in mammography diagnosis. Supervised MC detectors require learning from extracted individual MCs and MC clusters. However, they are limited by number of datasets given that MC images are hard to obtain. In this work, we propose a method to detect malignant microcalcification (MC) clusters using unsupervised, one-class, deep convolutional autoencoder. Specifically, we designed a deep autoencoder model where only patches extracted from normal cases’ mammograms are used during training. We then applied our trained model on patches extracted from testing images. Our training dataset contains 408 normal subjects, including 1961 full-field digital mammography images. Our testing datasets contains 276 subjects. Specifically, 106 of them were patients diagnosed with Ductal Carcinoma In-Situ (DCIS); 70 of them were diagnosed with Invasive Ductal Carcinoma (IDC); the rest 100 are normal cases containing 484 negative screening mammograms. Patches extracted from DCIS and IDC cases (positive patches) contain MC clusters, whereas patches extracted from normal cases (negative patches) don’t. As the model is trained only on negative images that do not contain MCs, it cannot reconstruct MCs well, and thus, the reconstruction error will be larger on positive patches than negative patches. Our detection algorithm’s decision is made based on Max-Squared Error between autoencoder’s input and output patches. To confirm the results were not simply due to blurring, we then compared our designed detector with unsharp mask with Gaussian blur results. The results using the unsupervised autoencoder on testing patches with size 64×64 achieves an AUC result of 0.93. The best performance on testing patches using Gaussian blur with kernel size equal to 11has an overall AUC of 0.82.
Annotated data availability has always been a major limiting f actor for the development of algorithms in the field of computer aided diagnosis. The purpose of this study is to investigate the feasibility of using a conditional generative adversarial network (GAN) to synthesize high resolution mammography images with semantic control. We feed a binary mammographic texture map to the generator to synthesize a full-field digital-mammogram (FFDM). Our results show the generator quickly learned to grow anatomical details around the edges within the texture mask. However, we found the training unstable and the quality of generated images unsatisfactory due to the inherent limitation of latent space and sample space mapping by the pix2pix framework. In order to synthesize high resolution mammography images with semantic control, we identified the critical challenge is to build the efficient mappings of binary textures with a great variety of pattern realizations with the image domain.
Purpose: To determine whether domain transfer learning can improve the performance of deep features extracted from digital mammograms using a pre-trained deep convolutional neural network (CNN) in the prediction of occult invasive disease for patients with ductal carcinoma in situ (DCIS) on core needle biopsy.
Method: In this study, we collected digital mammography magnification views for 140 patients with DCIS at biopsy, 35 of which were subsequently upstaged to invasive cancer. We utilized a deep CNN model that was pre-trained on two natural image data sets (ImageNet and DTD) and one mammographic data set (INbreast) as the feature extractor, hypothesizing that these data sets are increasingly more similar to our target task and will lead to better representations of deep features to describe DCIS lesions. Through a statistical pooling strategy, three sets of deep features were extracted using the CNNs at different levels of convolutional layers from the lesion areas. A logistic regression classifier was then trained to predict which tumors contain occult invasive disease. The generalization performance was assessed and compared using repeated random sub-sampling validation and receiver operating characteristic (ROC) curve analysis.
Result: The best performance of deep features was from CNN model pre-trained on INbreast, and the proposed classifier using this set of deep features was able to achieve a median classification performance of ROC-AUC equal to 0.75, which is significantly better (p<=0.05) than the performance of deep features extracted using ImageNet data set (ROCAUC = 0.68).
Conclusion: Transfer learning is helpful for learning a better representation of deep features, and improves the prediction of occult invasive disease in DCIS.
Predicting whether ductal carcinoma in situ (DCIS) identified at core biopsy contains occult invasive disease is an import task since these “upstaged” cases will affect further treatment planning. Therefore, a prediction model that better classifies pure DCIS and upstaged DCIS can help avoid overtreatment and overdiagnosis. In this work, we propose to improve this classification performance with the aid of two other related classes: Atypical Ductal Hyperplasia (ADH) and Invasive Ductal Carcinoma (IDC). Our data set contains mammograms for 230 cases. Specifically, 66 of them are ADH cases; 99 of them are biopsy-proven DCIS cases, of whom 25 were found to contain invasive disease at the time of definitive surgery. The remaining 65 cases were diagnosed with IDC at core biopsy. Our hypothesis is that knowledge can be transferred from training with the easier and more readily available cases of benign but suspicious ADH versus IDC that is already apparent at initial biopsy. Thus, embedding both ADH and IDC cases to the classifier will improve the performance of distinguishing upstaged DCIS from pure DCIS. We extracted 113 mammographic features based on a radiologist’s annotation of clusters.Our method then added both ADH and IDC cases during training, where ADH were “force labeled” or treated by the classifier as pure DCIS (negative) cases, and IDC were labeled as upstaged DCIS (positive) cases. A logistic regression classifier was built based on the designed training dataset to perform a prediction of whether biopsy-proven DCIS cases contain invasive cancer. The performance was assessed by repeated 5-fold CrossValidation and Receiver Operating Characteristic(ROC) curve analysis. While prediction performance with only training on DCIS dataset had an average AUC of 0.607(%95CI, 0.479-0.721). By adding both ADH and IDC cases for training, we improved the performance to 0.691(95%CI, 0.581-0.801).
Medical oncologists increasingly rely on expensive genomic analysis to stratify patients for different treatment. The genomic markers are able to divide patients into groups that behave differently in terms of tumor presentation, likelihood of metastatic spread, and response to chemotherapy and radiation therapy. In recent years there has been a rapid increase in the number of genomic tests available, like the Oncotype DX test, which provides the risk of cancer recurrence for a subset of patients. Radiogenomics, a new field that investigates the relationship between imaging phenotypes and genomic characteristics, may offer a less expensive and less invasive imaging surrogate for molecular subtype and Oncotype DX recurrence score (ODRS). This retrospective study analyzes the relationship between Breast Imaging-Reporting and Data System (BI-RADS) features as assessed by radiologists on mammograms with molecular subtype and ODRS. We used data from patients with BI-RADS features (shape or margin) and a genomic feature (subtype or ODRS) for the following cohort: shape vs. subtype (n=69), margin vs. subtype (n=78), shape vs. ODRS (n=20), and margin vs. ODRS (n=18). The association between features was assessed using a Fisher’s exact test. Our results show that shape assessed by radiologists according to the BI-RADS lexicon is associated with molecular subtype (p=0.0171), while BI-RADS features of shape and margin were not significantly associated with ODRS (p=0.7839, p=0.6047 respectively).
Reducing the overdiagnosis and overtreatment associated with ductal carcinoma in situ (DCIS) requires accurate prediction of the invasive potential at cancer screening. In this work, we investigated the utility of pre-operative histologic and mammographic features to predict upstaging of DCIS. The goal was to provide intentionally conservative baseline performance using readily available data from radiologists and pathologists and only linear models. We conducted a retrospective analysis on 99 patients with DCIS. Of those 25 were upstaged to invasive cancer at the time of definitive surgery. Pre-operative factors including both the histologic features extracted from stereotactic core needle biopsy (SCNB) reports and the mammographic features annotated by an expert breast radiologist were investigated with statistical analysis. Furthermore, we built classification models based on those features in an attempt to predict the presence of an occult invasive component in DCIS, with generalization performance assessed by receiver operating characteristic (ROC) curve analysis. Histologic features including nuclear grade and DCIS subtype did not show statistically significant differences between cases with pure DCIS and with DCIS plus invasive disease. However, three mammographic features, i.e., the major axis length of DCIS lesion, the BI-RADS level of suspicion, and radiologist’s assessment did achieve the statistical significance. Using those three statistically significant features as input, a linear discriminant model was able to distinguish patients with DCIS plus invasive disease from those with pure DCIS, with AUC-ROC equal to 0.62. Overall, mammograms used for breast screening contain useful information that can be perceived by radiologists and help predict occult invasive components in DCIS.
Predicting the risk of occult invasive disease in ductal carcinoma in situ (DCIS) is an important task to help address the overdiagnosis and overtreatment problems associated with breast cancer. In this work, we investigated the feasibility of using computer-extracted mammographic features to predict occult invasive disease in patients with biopsy proven DCIS. We proposed a computer-vision algorithm based approach to extract mammographic features from magnification views of full field digital mammography (FFDM) for patients with DCIS. After an expert breast radiologist provided a region of interest (ROI) mask for the DCIS lesion, the proposed approach is able to segment individual microcalcifications (MCs), detect the boundary of the MC cluster (MCC), and extract 113 mammographic features from MCs and MCC within the ROI. In this study, we extracted mammographic features from 99 patients with DCIS (74 pure DCIS; 25 DCIS plus invasive disease). The predictive power of the mammographic features was demonstrated through binary classifications between pure DCIS and DCIS with invasive disease using linear discriminant analysis (LDA). Before classification, the minimum redundancy Maximum Relevance (mRMR) feature selection method was first applied to choose subsets of useful features. The generalization performance was assessed using Leave-One-Out Cross-Validation and Receiver Operating Characteristic (ROC) curve analysis. Using the computer-extracted mammographic features, the proposed model was able to distinguish DCIS with invasive disease from pure DCIS, with an average classification performance of AUC = 0.61 ± 0.05. Overall, the proposed computer-extracted mammographic features are promising for predicting occult invasive disease in DCIS.
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